Standard Framework Construction of Technology and Equipment for Big Data in Crop Phenomics

Weiliang Wen, Shenghao Gu, Ying Zhang, Wanneng Yang, Xinyu Guo

Engineering ›› 2024, Vol. 42 ›› Issue (11) : 175-184.

PDF(3885 KB)
PDF(3885 KB)
Engineering ›› 2024, Vol. 42 ›› Issue (11) : 175-184. DOI: 10.1016/j.eng.2024.06.001
Research

Standard Framework Construction of Technology and Equipment for Big Data in Crop Phenomics

Author information +
History +

Abstract

Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional genomics, digital breeding, and smart cultivation. Despite this advancement, the lack of standards for the creation and usage of crop phenomics technology and equipment has become a bottleneck, limiting the industry’s high-quality development. This paper begins with an overview of the crop phenotyping industry and presents an industrial mapping of technology and equipment for big data in crop phenomics. It analyzes the necessity and current state of constructing a standard framework for crop phenotyping. Furthermore, this paper proposes the intended organizational structure and goals of the standard framework. It details the essentials of the standard framework in the research and development of hardware and equipment, data acquisition, and the storage and management of crop phenotyping data. Finally, it discusses promoting the construction and evaluation of the standard framework, aiming to provide ideas for developing a high-quality standard framework for crop phenotyping.

Graphical abstract

Keywords

Crop phenomics / Big data / Phenotyping technology and equipment / Standard framework / Industrial mapping

Cite this article

Download citation ▾
Weiliang Wen, Shenghao Gu, Ying Zhang, Wanneng Yang, Xinyu Guo. Standard Framework Construction of Technology and Equipment for Big Data in Crop Phenomics. Engineering, 2024, 42(11): 175‒184 https://doi.org/10.1016/j.eng.2024.06.001

References

[1]
S.A. Osinga, D. Paudel, S.A. Mouzakitis, I.N. Athanasiadis. Big data in agriculture: between opportunity and solution. Agric Syst, 195 (2022), Article 103298
[2]
J.L. Araus, J.E. Cairns. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci, 19 (1) (2014), pp. 52-61
[3]
R.T. Furbank, M. Tester. Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci, 16 (12) (2011), pp. 635-644
[4]
A. Zavafer, H. Bates, C. Mancilla, P.J. Ralph. Phenomics: conceptualization and importance for plant physiology. Trends Plant Sci, 2439 (9) (2023), pp. 1004-1013
[5]
C. Zhao, Y. Zhang, J. Du, X. Guo, W. Wen, S. Gu, et al. Crop phenomics: current status and perspectives. Front Plant Sci, 10 (2019), p. 714
[6]
X. Jin, P.J. Zarco-Tejada, U. Schmidhalter, M.P. Reynolds, M.J. Hawkesford, R.K. Varshney, et al. High-throughput estimation of crop traits: a review of ground and aerial phenotyping platforms. IEEE Geosci Remote Sens Mag, 9 (1) (2020), pp. 200-231
[7]
W. Yang, H. Feng, X. Zhang, J. Zhang, J.H. Doonan, W.D. Batchelor, et al. Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Mol Plant, 13 (2) (2020), pp. 187-214
[8]
D. Sun, K. Robbins, N. Morales, Q. Shu, H. Cen. Advances in optical phenotyping of cereal crops. Trends Plant Sci, 27 (2) (2021), pp. 191-208
[9]
S. Ninomiya. High-throughput field crop phenotyping: current status and challenges. Breed Sci, 72 (1) (2022), pp. 3-18
[10]
J.L. Araus, S.C. Kefauver, M. Zaman-Allah, M.S. Olsen, J.E. Cairns. Translating high-throughput phenotyping into genetic gain. Trends Plant Sci, 23 (5) (2018), pp. 451-466
[11]
C. Zhao. Big data of plant phenomics and its research progress. J Agric Big Data, 1 (2019), pp. 5-14Chinese
[12]
Deng CH, Naithani S, Kumari S, Cobo-Simon I, Quezada-Rodriguez EH, Skrabisova M, et al. Agricultural sciences in the big data era: genotype and phenotype data standardization, utilization and integration. DATABASE-OXFORD 2023; 2023:baad088.
[13]
E.A. Papoutsoglou, D. Faria, D. Arend, E. Arnaud, I.N. Athanasiadis, I. Chaves, et al. Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytol, 227 (1) (2020), pp. 260-273
[14]
D. Reynolds, F. Baret, C. Welckere, A. Bostrom, J. Ball, F. Cellini, et al. What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Sci, 282 (2019), pp. 14-22
[15]
W. Wang, W. Guo, L. Le, J. Yu, Y. Wu, D. Li, et al. Integration of high-throughput phenotyping, GWAS, and predictive models reveals the genetic architecture of plant height in maize. Mol Plant, 16 (2) (2023), pp. 354-373
[16]
S. Wolfert, L. Ge, C. Verdouw, M.J. Bogaardt. Big data in smart farming—a review. Agric Syst, 153 (2017), pp. 69-80
[17]
P. Song, J. Wang, X. Guo, W. Yang, C. Zhao. High-throughput phenotyping: breaking through the bottleneck in future crop breeding. Crop J, 9 (3) (2021), pp. 633-645
[18]
A. Watson, S. Ghosh, M.J. Williams, W.S. Cuddy, J. Simmonds, M.D. Rey, et al. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat Plants, 4 (1) (2018), pp. 23-29
[19]
M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 3 (1) (2016), Article 160018
[20]
C. Pommier, C. Michotey, G. Cornut, P. Roumet, E. Duchêne, R. Flores, et al. Applying FAIR principles to plant phenotypic data management in GnpIS. Plant Phenomics, 2019 ( 2019), Article 1671403
[21]
P. Krajewski, D. Chen, H. Cwiek, A.D.J. van Dijk, F. Fiorani, P. Kersey, et al. Towards recommendations for metadata and data handling in plant phenotyping. J Exp Bot, 66 (18) (2015), pp. 5417-5427
[22]
H. Cwiek-Kupczynska, T. Altmann, D. Arend, E. Arnaud, D.J. Chen, G. Cornut, et al. Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods, 12 (2016), p. 44
[23]
A.I. Ugochukwu, P.W.B. Phillips. Data sharing in plant phenotyping research: perceptions, practices, enablers, barriers and implications for science policy on data management. Plant Phenome Journal, 5 (1) (2022), Article e20056
[24]
J. Fan, Y. Li, S. Yu, W. Gou, X. Guo, C. Zhao. Application of internet of things to agriculture—the LQ-FieldPheno platform: a high-throughput platform for obtaining crop phenotypes in field. Research, 2023 ( 2023), p. 0059
[25]
J. Du, J. Fan, C. Wang, X. Lu, Y. Zhang, W. Wen, et al. Greenhouse-based vegetable high-throughput phenotyping platform and trait evaluation for large-scale lettuces. Comput Electron Agric, 186 (2021), Article 106193
[26]
S. Wu, W. Wen, Y. Wang, J. Fan, C. Wang, W. Gou, et al. MVS-Pheno: a portable and low-cost phenotyping platform for maize shoots using Multiview stereo 3D reconstruction. Plant Phenomics, 2020 ( 2020), Article 1848437
[27]
S. Cai, W. Gou, W. Wen, X. Lu, J. Fan, X. Guo. Design and development of a low-cost UGV 3D phenotyping platform with integrated LiDAR and electric slide rail. Plants, 12 (3) (2023), p. 483
[28]
X. Xiong, L. Yu, W. Yang, M. Liu, N. Jiang, D. Wu, et al. A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage. Plant Methods, 13 (2017), p. 7
[29]
W. Yang, Z. Guo, C. Huang, L. Duan, G. Chen, N. Jiang, et al. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun, 5 (2014), p. 5087
[30]
Y. Zhang, J. Wang, J. Du, Y. Zhao, X. Lu, W. Wen, et al. Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high-throughput phenotypic analysis. Plant Biotechnol J, 19 (1) (2020), pp. 35-50
[31]
J.J. Du, X.J. Lu, J.C. Fan, Y.J. Qin, X.Z. Yang, X.Y. Guo. Image-based high-throughput detection and phenotype evaluation method for multiple lettuce varieties. Front. Plant Sci., 11 (2020), Article 563386
[32]
J. Gao, X. Hu, C. Gao, G. Chen, H. Feng, Z. Jia, et al. Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat. Plant Biotechnol J, 21 (10) (2023), pp. 1966-1977
[33]
X. Zhang, C. Huang, D. Wu, F. Qiao, W. Li, L. Duan, et al. High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol, 173 (3) (2017), pp. 1554-1564
[34]
T. Miao, W. Wen, Y. Li, S. Wu, C. Zhu, X. Guo. Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots. Gigascience, 10 (5) (2021), Article giab031s
[35]
Y. Li, W. Wen, J. Fan, W. Gou, S. Gu, X. Lu, et al. Multi-source data fusion improves time-series phenotype accuracy in maize under a field high-throughput phenotyping platform. Plant Phenomics, 5 (2023), p. 0043
[36]
D. Wu, L.J. Yu, J.L. Ye, R.F. Zhai, L.F. Duan, L.B. Liu, et al. Panicle-3D: a low-cost 3D-modeling method for rice panicles based on deep learning, shape from silhouette, and supervoxel clustering. Crop J, 10 (5) (2022), pp. 1386-1398
[37]
X. Liang, X. Xu, Z. Wang, L. He, K. Zhang, B. Liang, et al. StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model. Plant Biotechnol J, 20 (3) (2022), pp. 577-591
[38]
P. Neveu, A. Tireau, N. Hilgert, V. Negre, J. Mineau-Cesari, N. Brichet, et al. Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. New Phytol, 221 (1) (2019), pp. 588-601
[39]
D. Reynolds, J. Ball, A. Bauer, R. Davey, S. Griffiths, J. Zhou. CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. Gigascience, 8 (3) (2019), Article giz009
[40]
Y. Xu, X. Zhang, H. Li, H. Zheng, J. Zhang, M.S. Olsen, et al. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Mol Plant, 15 (11) (2022), pp. 1664-1695
AI Summary AI Mindmap
PDF(3885 KB)

Accesses

Citations

Detail

Sections
Recommended

/